US11332149B1 - Determining acceptable driving behavior based on vehicle specific characteristics - Google Patents

Determining acceptable driving behavior based on vehicle specific characteristics Download PDF

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US11332149B1
US11332149B1 US16/861,080 US202016861080A US11332149B1 US 11332149 B1 US11332149 B1 US 11332149B1 US 202016861080 A US202016861080 A US 202016861080A US 11332149 B1 US11332149 B1 US 11332149B1
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Prior art keywords
vehicle
particular vehicle
driver
owner
driving behavior
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US16/861,080
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Brian N. Harvey
Matthew Eric Riley, SR.
Joseph Robert Brannan
J. Lynn Wilson
Ryan Gross
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State Farm Mutual Automobile Insurance Co
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State Farm Mutual Automobile Insurance Co
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Priority to US16/861,080 priority Critical patent/US11332149B1/en
Assigned to STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY reassignment STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BRANNAN, JOSEPH ROBERT, GROSS, RYAN, HARVEY, BRIAN N., RILEY, MATTHEW ERIC, SR, WILSON, J. LYNN
Priority to US17/745,036 priority patent/US20220274605A1/en
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09Driving style or behaviour
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce
    • G06Q30/06Buying, selling or leasing transactions
    • G06Q30/0645Rental transactions; Leasing transactions
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/30Transportation; Communications
    • G06Q50/40
    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07CTIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00Registering or indicating the working of vehicles
    • G07C5/008Registering or indicating the working of vehicles communicating information to a remotely located station

Definitions

  • the present disclosure generally relates to vehicle telematics and, more specifically, to systems and methods for applying one or more penalties and/or incentives to a driver of a rented vehicle based on telematics data.
  • the telematics data may be collected and analyzed to determine the acceleration, braking and/or cornering habits of a driver of a vehicle, and the results of the analysis may be used to measure the performance of the driver over time.
  • the telematics data may be generated by sensors on the vehicle, or by a mobile device (e.g., smart phone) carried by the driver, for example.
  • the measured performance may then be used for various purposes, such as modifying an insurance rating of the driver.
  • telematics data also be used in connection with car rental services, including peer-to-peer car rentals, to score potential renters (e.g., so that vehicle owners may avoid renting their vehicles to certain types of drivers).
  • car rental services utilize terms of a rental agreement to constrain renter use of the rented vehicle.
  • such terms are not able to constrain or limit many driving behaviors of a renter while the renter is operating a vehicle. This may occur because car rental services are not able to detect such driving behaviors, and as a result the car rental services cannot incorporate such driving behaviors into a rental agreement.
  • car rental services may rely on the vehicle owner trusting the renter to not misuse the rented vehicle.
  • vehicle owners may hesitate to trust renters they do not personally know. Unconstrained use of the rented vehicle on the part of the renter may result in increased maintenance and the associated costs and time for the vehicle owner. For example, frequent hard braking by renters may require that the owner replace the brake pads and rotors more often. This is especially problematic for vehicle owners in peer-to-peer vehicle rental services.
  • the rented vehicle may be their primary vehicle, and associated costs and times of maintenance may be unmanageable.
  • the present embodiments may, inter alia, utilize telematics data indicative of operation of a rented vehicle by the driver/renter during a period of time to apply one or more penalties and/or incentives to the driver, as agreed upon in the terms for renting the vehicle from the owner.
  • the embodiments may establish trust between the owner of the vehicle and renter (or at least, provide the owner with a certain comfort level even if the renter cannot be fully trusted) by detecting and incentivizing or penalizing driving behaviors.
  • a method of incentivizing and/or penalizing vehicle renters may include receiving, at one or more processors, an indication that a driver has agreed to terms for renting a vehicle from an owner of the vehicle. The terms may include potential application of penalties or incentives to the driver based on driving behavior.
  • the method may also include receiving, at the one or more processors, telematics data collected over a period of time. The telematics data may be indicative of operation of the rented vehicle by the driver during the period of time.
  • the method may also include identifying, by the one or more processors analyzing the telematics data, one or more driving behaviors of the driver during the period of time.
  • the method may further include identifying, by the one or more processors, one or more characteristics of the rented vehicle.
  • the method may also include causing, by the one or more processors, one or more penalties or incentives to be applied to the driver. The one or more penalties may be based on the one or more driving behaviors and the one or more characteristics of the rented vehicle.
  • a tangible, non-transitory computer-readable medium may store instructions that, when executing by one or more processors, cause one or more processors to receive an indication that a driver has agreed to terms for renting a vehicle from an owner of the vehicle. The terms may include potential application of penalties or incentives to the driver based on driving behavior.
  • the instructions may also cause the one or more processors to receive telematics data collected over a period of time. The telematics data may be indicative of operation of the rented vehicle by the driver during the period of time.
  • the instructions may also cause the one or more processors to identify one or more driving behaviors of the driver during the period of time.
  • the instructions may also cause the one or more processors to identify one or more characteristics of the rented vehicle.
  • the instructions may also cause the one or more processors to cause one or more penalties or incentives to be applied to the driver.
  • the one or more penalties may be based on the one or more driving behaviors and the one or more characteristics of the rented vehicle.
  • FIG. 1 illustrates an exemplary environment in which penalties and/or incentives may be applied to a renter based on telematics data.
  • FIG. 2 illustrates an exemplary chart of characteristics of a rented vehicle.
  • FIG. 3A illustrates an exemplary user interface by which a vehicle owner may establish vehicle rental terms.
  • FIG. 3B illustrates an exemplary user interface at which a vehicle owner may be notified that one or more penalties and/or incentives have been applied.
  • FIG. 4 illustrates an exemplary method of which may cause one or more penalties and/or incentives to be applied.
  • aspects of the present invention relate to vehicle rental platforms wherein telematics data indicative of operation of the rented vehicle by a driver/renter may be used to apply one or more penalties and/or incentives to the driver, e.g., as agreed upon in the rental terms.
  • the vehicle rental platform may be a peer-to-peer vehicle rental platform or a business to consumer vehicle rental platform, for example.
  • a vehicle owner may establish terms for renting a vehicle, e.g., via a user interface of the vehicle rental platform.
  • a potential renter of the owner's vehicle may have access to such terms before agreeing to rent the owner's vehicle.
  • a potential vehicle renter may be required to agree to the established terms before renting the owner's vehicle. If a renter has agreed to the terms for renting the owner's vehicle, the vehicle owner may receive an indication that the renter has agreed to the terms. The indication may include the terms themselves.
  • a telematics data collection device may collect telematics data. Telematics data may only be collected, stored and/or used if the individual (i.e., renter or owner) has authorized such collection/storage/use. Authorization may occur, for example, upon the renter and the owner signing up to the vehicle rental platform.
  • the telematics data collection device may include one or more components of a sensor/telematics system within the rented vehicle and/or may include a personal electronic device (e.g., a smart phone) of the renter.
  • the telematics data collection device may also include sensors and/or subsystems that generate the telematics data itself, or may simply collect the telematics data after another device or devices has/have generated the telematics data.
  • the telematics data may include any sensed or monitored data that indicates the operation of the rented vehicle by the driver.
  • the telematics data collection device may collect data over a period of time during which the renter is operating the rented vehicle. The period of time may be the duration of a trip of the rented vehicle or a duration of a rental period of the rented vehicle, for example.
  • a remote server may identify one or more driving behaviors occurring during the period of time, with the driving behavior(s) being determined from the telematics data.
  • the driving behavior(s) may be associated with turning behavior, braking behavior, and/or accelerating behavior, for example. Identifying the driving behavior(s) may include identifying one or more values associated with the driving behavior(s), such as values associated with acceleration, speed, and/or lateral force.
  • acceleration may refer to either positive or negative acceleration (i.e., deceleration), unless the context of the usage makes it clear that only positive or only negative acceleration is contemplated.
  • the value(s) may be telematics data values (e.g., sensor readings, operational parameters of the vehicle such as speed, etc.) or normalized or averaged telematics data values, for example.
  • the one or more values may be values calculated using telematics data (e.g., a weighted sum of multiple telematics data values).
  • a “driving behavior” may be a behavior that occurs during a particular moment in time (e.g., a single hard braking event), or a behavior that occurs over a longer duration of time (e.g., an average number of hard braking events per hour).
  • Each rented vehicle may have one or more vehicle characteristics (e.g., make, model, a particular design such as rear-wheel or all-wheel drive, a particular feature such as cruise control, vehicle age, number of miles driven, maintenance).
  • the remote server may determine the one or more characteristics of the rented vehicle based on telematics data, such as sensor data associated with the rented vehicle.
  • the remote server may also, or instead, determine characteristics of the rented vehicle from data received from the vehicle owner or a third party.
  • the remote server may obtain the one or more characteristics of the rented vehicle.
  • the remote server may apply one or more penalties and/or incentives to the renter operating the rented vehicle. These penalties and/or incentives may be in accordance with the agreed upon terms between the vehicle renter and the vehicle owner.
  • the remote server may apply the one or more penalties and/or incentives immediately after a driving behavior occurs, or at a later time.
  • the remote server may apply the one or more penalties and/or incentives in response to a single instance of a driving behavior, or in response to multiple instances of a driving behavior or multiple instances of different driving behaviors.
  • Causing an incentive and/or penalty to be applied to the driver may include comparing one or more values associated with the driving behavior(s) (as discussed above) to one or more threshold values. For example, a penalty may be applied if the one or more values fall outside a threshold value, and/or an incentive may be applied if the one or more values stay within a threshold value.
  • Threshold values may vary based on the one or more characteristics of the rented vehicle. For example, a threshold value for “negative acceleration” (which may be indicative of how “hard” the driver brakes) may be lower for a sedan than for a sports car, or a threshold value for “negative acceleration” may be lower for a minivan than for a sedan, etc. Threshold values may also vary based on characteristics of a renter operating the rental vehicle. For example, a threshold value for “g-force” may be lower for a young driver than for an middle-aged driver. The threshold values may be associated with one or more of acceleration, speed, or lateral force of the rental vehicle during operation by the renter, for example.
  • the remote server may transmit an electronic message to the owner of the rented vehicle notifying the owner that a penalty and/or incentive has been applied to the renter operating the rented vehicle.
  • the remote server may transmit the notification to the owner immediately after the penalty and/or incentive has been applied.
  • the remote server may instead transmit the notification at the end of the rental period by the renter, or at the next instance of a regular interval of time during the rental period. In other embodiments, the notification may be transmitted at any other suitable time.
  • the remote server may transmit an electronic message to the renter of the rented vehicle notifying the renter that a penalty and/or incentive has been applied to the renter.
  • the remote server may transmit the notification to the renter immediately after the penalty and/or incentive has been applied, at the end of the rental period by the renter, or at the next instance of a regular interval of time during the rental period.
  • the remote server may transmit the notification when the rented vehicle is known to be stopped, such as when the vehicle is at a traffic light or the rented vehicle is parked.
  • the vehicle may be determined to be stopped using telematics data (e.g., time stamped data from a GPS unit of the renter's mobile device or the vehicle, or data from an inertial measurement unit of the renter's mobile device or the vehicle).
  • telematics data e.g., time stamped data from a GPS unit of the renter's mobile device or the vehicle, or data from an inertial measurement unit of the renter's mobile device or the vehicle.
  • the notification may be applied at any suitable other time.
  • FIG. 1 depicts an exemplary environment 100 in which penalties and/or incentives may be applied to a renter based on telematics data.
  • the environment 100 includes a vehicle 114 .
  • the vehicle 114 may include a data collection device (not shown in FIG. 1 ) that collects various types of telematics data.
  • the vehicle 114 may also include multiple sensors (not shown in FIG. 1 ) that collect various types of telematics data.
  • the vehicle 114 may also carry a renter mobile device 120 and the renter mobile device 120 may collect various types of telematics data.
  • the telematics data collected at the vehicle 114 may include any data that may be sensed or monitored, and may be used to calculate or otherwise infer driving behaviors of the renter.
  • the data may include any one or more of speed information, acceleration information, braking information, steering information, location/position information (e.g., collected by a vehicular global positioning system (GPS) device), translational and/or rotational g-force information (e.g., collected by a gyroscope device), on-board diagnostic information, information collected by a camera, video camera, LiDAR, radar or other device sensing an environment external to the vehicle (e.g., sensing proximity to other vehicles or other objects, orientation with respect to other vehicles or other objects, etc.), automated safety and/or control system information (e.g., adaptive cruise control status and/or when cruise control is engaged/disengaged, forward and/or rear collision warning system outputs, lane departure system outputs, electronic stability control system status, etc.), and so on.
  • the vehicle 114 may include any one
  • the vehicle 114 may send collected telematics data to a server 102 via a communication link. Additionally or alternatively, the renter mobile device 120 may send collected telematics data to the server 102 via a communication link. For example, the vehicle 114 may send collected telematics data over a first communication link and the renter mobile device 120 may send collected telematics data over a second communication link. Additionally, the vehicle 114 and/or the renter mobile device 120 may send collected telematics data to server 102 via one or more transmissions.
  • telematics data telematics data may be collected at vehicle 114 and/or renter mobile device 120 over the course of a rented vehicle trip and sent to the server 102 in a single transmission.
  • telematics data may be collected at vehicle 114 and/or renter mobile device 120 at regular intervals and sent to the server 102 in multiple transmissions (e.g., after each regular interval).
  • the transmissions may be sent to server 102 via a wireless transmitter or transceiver that is coupled to the vehicle 114 .
  • the vehicle 114 may be equipped with a Bluetooth system that provides the telematics data to a smart phone or other portable communication device of the driver or a passenger, such as renter mobile device 120 , and the smart phone or other portable communication device may transmit the data to the server 102 via a wireless (e.g., cellular) network.
  • the vehicle 114 may include an interface to a portable memory device, such as a portable hard drive or flash memory device.
  • the portable memory device may be used to download data from the data collection device and then manually carried to the server 102 .
  • the portable memory device may be used to download telematics data from the data collection device to a driver's or passenger's computer device (e.g., a desktop computer, laptop computer, smartphone, etc.), which may in turn be used to transmit the telematics data to the server 102 via one or more wired and/or wireless networks.
  • a driver's or passenger's computer device e.g., a desktop computer, laptop computer, smartphone, etc.
  • the server 102 may include a processor 104 , a memory 106 , a communication interface 108 , a telematics data analysis unit 110 , and a penalty/incentive application unit 112 , each of which will be described in more detail below.
  • the processor 104 may include one or more processors (e.g., a central processing unit (CPU)) adapted and configured to execute various software applications and components of the server 102 .
  • the memory 106 may include one or more memories of one or more types (e.g., a solid state memory, a hard drive, etc.), and may include data storage containing a plurality of software applications, data used and/or output by such software applications, and/or a plurality of software routines, for example.
  • the communication interface 108 may include hardware (e.g., one or more physical ports, one or more network interface cards, one or more hardware or firmware processors, etc.) and/or software (e.g., software executed by the processor 104 or one or more other processors of the server 102 ) configured to enable server 102 to receive transmissions of data collected by different data collection devices and mobile devices (e.g., associated with different vehicles similar to the vehicle 114 ). If the data transmissions are made via the Internet, for example, the communication interface 108 may include an Ethernet port. Telematics data may be received at the communication interface 108 from the vehicle 114 , the renter mobile device 120 , or any other device located within the vehicle 114 .
  • hardware e.g., one or more physical ports, one or more network interface cards, one or more hardware or firmware processors, etc.
  • software e.g., software executed by the processor 104 or one or more other processors of the server 102
  • the communication interface 108 may include an Ethernet port. Telematics data may be
  • the telematics data analysis unit 110 may analyze the received telematics data. Telematics data analysis unit 110 may only receive and analyze telematics data corresponding to a rental duration of the rented vehicle by the renter, for example. Alternatively, the telematics data unit 110 may filter received telematics data to exclude any telematics data that does not correspond to the rental duration. By analyzing the telematics data, the telematics data analysis unit 110 may determine one or more driving behaviors of the renter during the period of time, and may determine a time and/or a location associated with each such driving behavior.
  • the telematics data analysis unit 110 may also determine one or more characteristics of the vehicle 114 using telematics data of the vehicle 114 .
  • the odometer may indicate the number of miles driven by the vehicle 114 .
  • Sensors on or in the tires may indicate the quality of the tires, the air pressure, and/or the date of last replacement (e.g., abrupt changes in the rate of tire spinning may indicate worn tires), for example.
  • telematics data analysis unit 110 may determine the one or more characteristics of the vehicle 114 by analyzing data received from the rented vehicle characteristics server 116 .
  • the rented vehicle characteristics server 116 may store or otherwise provide access to rented vehicle characteristics information, and may include only a single server or multiple servers.
  • the rented vehicle characteristics server 116 may be owned and operated by a third party, for example.
  • the telematics data analysis unit 110 may determine one or more characteristics of the vehicle 114 by using an identifier of the vehicle 114 (e.g., a VIN of the vehicle 114 ) to locate matching data in a database maintained by the rented vehicle characteristics server 116 .
  • the penalty/incentive application unit 112 may apply one or more penalties and/or incentives to the driver of the rented vehicle 114 .
  • the penalty/incentive application unit 112 may compare values associated with the driving behavior(s) to threshold values. Initially, in some embodiments, the owner of the vehicle 114 initially sets the threshold values (e.g., via the owner mobile device 122 when executing a peer-to-peer vehicle sharing application). Alternatively, server 102 (e.g., the penalty/incentive application unit 112 ) may initially set the thresholds to default values.
  • the threshold values may then be modified based on the one or more characteristics of the rented vehicle 114 before being agreed upon by the owner and the renter as terms of the rental.
  • the server 102 e.g., the penalty/incentive application unit 112
  • a penalty or incentive may be applied if the values associated with the driving behavior fall within or exceed the threshold values.
  • the penalty and/or incentive may be applied to an account of the renter associated with or linked to the vehicle rental platform, for example.
  • one, some or all of the threshold values may be automatically set or modified based on the one or more characteristics of the rented vehicle 114 in which the driving behavior or set of driving behaviors occurred.
  • the threshold values may be set or modified to reflect that hard braking behavior is less acceptable because the vehicle 114 is known to have poor quality brakes (e.g., as detected by a sensor at the brakes of the vehicle 114 ) relative to most other vehicles.
  • the threshold value for “negative acceleration” may be lowered, or set to a relatively low level.
  • a threshold value for “negative acceleration” may be lower for a sedan than for a sport vehicle, and lower still for a minivan than a sedan.
  • the penalty/incentive application unit 112 may optionally utilize a driving score to determine if a penalty and/or incentive should be applied.
  • driving scores may be calculated and stored by a driving score server 118 , and the server 102 may access the driving scores by communicating with the driving score server 118 (e.g., via a long-range communication network such as the Internet).
  • the penalty/incentive application unit 112 or another component of the server 102 may calculate a driving score, and the server 102 may store the score in memory 106 or another memory.
  • the penalty/incentive application unit 112 calculates a driving score but the driving score server 118 stores the driving score. In such an embodiment, the server 102 may transmit the driving score to the driving score server 118 to be stored.
  • the driving score may be calculated based on the driving behavior(s), and possibly also the one or more characteristics of the rented vehicle. For example, an instance of hard braking may result in a lower driving score if the instance of hard braking occurred in a minivan as opposed to a sedan, and/or an instance of excessive acceleration may result in a lower driving score if the acceleration occurred in a vehicle that needs tire replacement as opposed to a vehicle on which the tires were recently replaced.
  • penalty/incentive application unit 112 need not modify any of its thresholds (discussed above) based on the one or more characteristics of the rented vehicle.
  • the penalty/incentive application unit 112 may optionally notify the owner of the vehicle 114 that a penalty or an incentive has been applied to the renter of the vehicle 114 .
  • the server 102 may send this notification to the owner mobile device 122 , for example.
  • the server 102 and more specifically the communication interface 108 may transmit the notification to the owner mobile device 122 immediately after the penalty and/or incentive has been applied, at the end of the rental period by the renter, or at the next instance of a regular interval of time during the rental period, for example.
  • the server 102 may send the notification at any suitable other time.
  • the penalty/incentive application unit 112 may also, or instead, notify the driver of the vehicle 114 that a penalty or an incentive has been applied to the renter.
  • the server 102 may send this notification to the owner mobile device 120 , for example.
  • the server 102 and more specifically the communication interface 108 may transmit the notification to the renter mobile device 120 immediately after the penalty and/or incentive has been applied, at the end of the rental period by the renter, or at the next instance of a regular interval of time during the rental period, for example (e.g., with the same timing as notifications to the vehicle owner).
  • the server 102 may transmit the notification to the driver when the rented vehicle 114 is known to be stopped, such as when the vehicle 114 is at a traffic light or parked.
  • the server 102 may determine that the vehicle 114 is stopped using telematics data (e.g., time stamped data from a GPS unit of the renter mobile device 120 or the vehicle 114 , or data from an inertial measurement unit of the renter mobile device 120 or the vehicle 114 ). In other embodiments, the server 102 may send the notification at any suitable other time.
  • telematics data e.g., time stamped data from a GPS unit of the renter mobile device 120 or the vehicle 114 , or data from an inertial measurement unit of the renter mobile device 120 or the vehicle 114 .
  • vehicle characteristics 202 may include a make 204 , a model 206 , an age 208 of the vehicle, a total number of miles 210 traveled by the vehicle, a time 212 within which the last oil change was obtained for the vehicle, a number of miles 214 since the last oil change was obtained for the vehicle, a time 216 within which the last tire replacement was obtained for the vehicle, a number of miles 218 since the last tire replacement was obtained for the vehicle, a time 220 within which the last tune up was obtained for the vehicle, and a number of miles 222 since last tune up was obtained for the vehicle.
  • more or fewer vehicle characteristics may be included. Any number of the one or more vehicle characteristics may be utilized to cause a penalty and/or incentive to be applied. For example, the penalty or incentive application may be based on the make 204 and the model 206 of the vehicle only, or based on the make 204 , the model 206 , and the age 208 of the vehicle, etc. Similarly, any number of the one or more vehicle characteristics may be utilized to set or modify threshold values. For example, a threshold value for negative acceleration may be lower for a vehicle identified from the make 204 and the model 206 to be a minivan than a vehicle identified from the make 204 and the model 206 to be a sedan.
  • Each characteristic of the rented vehicle may have an associated value.
  • the make 204 has an associated value 224
  • the model 206 has an associated value 226
  • the age 208 has an associated value 228
  • the number of miles 210 has an associated value 230
  • the time 212 has an associated value 232
  • the number of miles 214 has an associated value 234
  • the time 216 has an associated value 236
  • the number of miles 218 has an associated value 238
  • the time 220 has an associated value 240
  • the number of miles 222 has an associated value 242 .
  • Each associated value may be an exact value or it may be a range of values.
  • the age 208 of the vehicle may be indicated as 10 years, as 10-12 years, as the purchase date of the vehicle, and so on.
  • Each vehicle characteristic may change and be updated at any given point. For example, the number of miles 210 driven by the vehicle may change as the vehicle continues to be driven. This may be recorded in the telematics data and updated at the rented vehicle characteristics server 116 , for example.
  • FIG. 3A illustrates an exemplary user interface 300 by which a vehicle owner may establish vehicle rental terms.
  • the exemplary user interface 300 may be presented via an owner's mobile device such as owner mobile device 122 or any other device of the vehicle owner.
  • the vehicle owner may establish vehicle rental terms by modifying driving behavior settings 302 .
  • the vehicle owner may establish vehicle rental terms at any time.
  • the driving behavior settings 302 may include settings of a turning behavior control 304 , settings of a braking behavior control 306 , and settings of an accelerating behavior control 308 .
  • the driving behavior settings 302 may include settings of fewer, more and/or different controls than those shown in FIG. 3A .
  • the vehicle owner may use the driving behavior settings 302 to establish vehicle rental terms.
  • the vehicle owner may establish acceptable turning behavior by modifying the turning behavior control 304 .
  • Acceptable turning behavior may be constrained, for example, by acceptable g-force on the vehicle.
  • Driving behavior settings 302 may be translated to threshold values by server 102 , for example.
  • An owner of the rented vehicle may set specific penalties and or incentives at the driving behavior settings 302 and more specifically at the turning behavior control 304 , braking behavior control 306 , and accelerating behavior control 308 options.
  • a vehicle owner whose vehicle has a delicate steering system may opt to set stricter penalties with the turning behavior control 304 than with other behavior controls (e.g., a vehicle owner may set a monetary fine of twenty-five dollars per instance of unacceptable turning behavior).
  • Exemplary user interface 300 may also provide other settings 310 that an owner of the rented vehicle may modify.
  • the settings 310 include settings of a geolocation preferences control 312 and a vehicle use preferences control 314 .
  • a vehicle owner may set a geolocation boundary on where the rented vehicle may be driven (e.g., a vehicle owner in the suburbs of a large city may wish to set a geolocation preference such that the rented vehicle stays in the suburbs and is not driven into the city).
  • vehicle use preferences control 314 an owner of the rented vehicle may set preferences more generally as to how a vehicle may be used (e.g., with respect to actions other than driving behaviors or location).
  • an owner may set preferences regarding the presence of children and/or pets in the vehicle, and/or whether the vehicle may be used for purposes such as moving heavy items (e.g., furniture or yard items).
  • Such activity may be detected by the rented vehicle, for example vehicle 114 , using a weight sensor at the seats or hatchback, a microphone, a camera, and/or any other suitable type of sensor(s).
  • a renter of the rented vehicle may be penalized or incentivized based on the settings of the turning behavior control 304 , braking behavior control 306 , accelerating behavior control 308 , geolocation preferences control 312 and/or vehicle use preferences control 314 , in combination with the corresponding portions of the telematics data.
  • the vehicle owner may also be able to modify or input information associated with the owner's account via an account settings control 316 .
  • the owner may make these modifications using a vehicle information control 318 and/or an account information control 320 , for example.
  • the vehicle owner may input information regarding the one or more characteristics of the rented vehicle 114 (e.g., make, model, year, etc.). Input of such information may be required before the vehicle owner is able to offer the vehicle 114 for rent.
  • the vehicle owner may further be prompted at vehicle information control 318 to periodically update information regarding the one or more characteristics of the rented vehicle 114 .
  • the server 102 may notify the renter and/or owner of the vehicle that the penalty has been applied.
  • FIG. 3B illustrates an exemplary user interface 350 via which a vehicle owner may receive a notification 352 indicating that one or more penalties and/or incentives have been applied.
  • the server 102 and more specifically the communication interface 108 may send notification 352 as soon as the penalty is applied, or at any other time.
  • the notification 352 may include the reason why a penalty or incentive was applied (as depicted in FIG. 3B ) or may not include such information.
  • the server 102 and more specifically the communication interface 108 may send a notification the same as or similar to notification 352 to the renter of the rented vehicle.
  • the notification may optionally allow the renter to access more information about the penalty.
  • the server 102 may send the notification to the renter immediately, or only when the rented vehicle is known to be stopped.
  • the notification may be sent when the rented vehicle is known to be turned off, for example.
  • a notification that a penalty is about to be applied may be sent to the renter. For example, if the renter has agreed to a “three strike” system for a penalty, a notification that the renter has incurred the second “strike” may be delivered to the renter.
  • FIG. 4 is a flow diagram of an exemplary method 400 of incentivizing and/or penalizing a vehicle renter.
  • the method 400 may be implemented by the processor 104 of the server 102 .
  • the term “server” may refer to a single computing device at a single location, or to a number of computing devices (e.g., distributed across a number of different locations).
  • an indication that a driver has agreed to terms for renting a vehicle from an owner of the vehicle may be received (block 402 ).
  • the terms may include potential application of penalties or incentives to the driver based on driving behavior.
  • the terms may be set by the owner of the rented vehicle, via a user interface accessed by the owner. For example, the owner may set the terms via the user interface 300 of FIG. 3A .
  • the terms may specify preferences relating to one or more particular driving behaviors.
  • the terms may specify certain instances in which driving behavior(s) is/are allowed or not allowed.
  • the terms may further specify other vehicle owner preferences, such as geolocation preferences, and/or vehicle use preferences other than driving behaviors. Terms may vary based on a profile of a vehicle renter, and/or based on the rented vehicle.
  • the driver may accept the terms explicitly or implicitly. In some embodiments, the driver accepts the terms via a user interface similar to that shown in FIG. 3A .
  • the terms may be transmitted to a sever implementing the method 400 , such as server 102 .
  • the terms may be stored in the memory of the server (e.g., memory 106 ), and may be used by a software unit (e.g., the penalty/incentive application unit 112 ) to determine if a penalty and/or incentive should be applied.
  • Telematics data collected over a period of time may be received (block 404 ).
  • the telematics data is indicative of the operation of the rented vehicle by the driver during the period of time.
  • the data collection device may be similar to any of the different implementations discussed above (e.g., including one or multiple components, etc.).
  • the vehicle telematics data includes a plurality of subsets of vehicle telematics data, with each subset corresponding to a different trip.
  • the vehicle telematics data may be received by any suitable technique(s), such as any of the techniques for obtaining vehicle telematics data described above in connection with FIG. 1 (e.g., transferring to/from a portable memory, using wired and/or wireless communications, etc.), for example.
  • the vehicle telematics data may include any data from which driving behaviors and/or features of the driving environment may be inferred or calculated.
  • the vehicle telematics data may include acceleration data, braking data, cornering data, g-force data, visual data, location data, etc., and may include data that was generated by an accelerometer, gyroscope, GPS device, camera, lidar, and/or one or more other units or sensor types.
  • the period of time may be the full duration of time during which the renter has rented the rented vehicle, the duration of a single trip by the renter in the rented vehicle, or any other duration of time.
  • the telematics data may be received at the server implementing the method 400 (e.g., at server 102 ).
  • the telematics data may be stored at the memory 106 and processed by the processor 104 (e.g., when executing instructions of the telematics data analysis unit 110 ), for example.
  • One or more driving behaviors of the driver during the period of time may be identified by analyzing the telematics data (block 406 ).
  • the one or more driving behaviors may be identified by the server (e.g., by the telematics data analysis unit 110 ).
  • the one of more driving behaviors may each correspond to certain portions of the telematics data, and may take place at a certain point in time (e.g., discrete driving events) or over a longer duration of time (e.g., average or maximum speeds, g-forces, etc., over a longer time period).
  • One or more characteristics of the rented vehicle may be identified (block 408 ).
  • the one or more characteristics of the rented vehicle may be determined from the telematics data, and/or based on rented vehicle characteristics data received from the vehicle owner or a third party server.
  • the one or more characteristics of the rented vehicle may include the make of the vehicle, the model of the vehicle, the age of the vehicle, the number of miles driven by the vehicle, the oil change status of the vehicle, the tire replacement status of the vehicle, the tune up status of the vehicle, and/or other characteristics.
  • One or more penalties or incentives may be applied (block 410 ).
  • the application of the one or more penalties or incentives may be based on the one or more driving behaviors and the one or more characteristics of the rented vehicle.
  • the one or more driving behaviors may trigger a penalty when the behavior(s) occur(s) in a vehicle with certain characteristics, but not in a vehicle with other characteristics.
  • the one or more driving behaviors may each correspond to one or more values (e.g., acceleration, speed, and/or lateral force).
  • the one or more penalties or incentives are applied if the one or more values fall outside (or stay within) a threshold value, wherein the threshold value may be based on the one or more characteristics of the rented vehicle (e.g., make of the rented vehicle, model of the rented vehicle, age of the rented vehicle, number of miles driven by the rented vehicle, and/or maintenance of the rented vehicle).
  • the one or more penalties or incentives are applied if a driving score does not meet a threshold, with the driving score being computed based on the one or more characteristics of the rented vehicle.
  • the renter of the vehicle and/or the owner of the vehicle may optionally be notified that a penalty has been applied (e.g., via the user interface of FIG. 3B ).
  • any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment.
  • the appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
  • the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion.
  • a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
  • “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

Abstract

In a method for applying penalties or incentives to a driver of a rented vehicle, an indication that the driver has agreed to terms for renting the vehicle from the vehicle owner is received, with the terms including the potential application of penalties or incentives to the driver based on driving behavior. Telematics data, indicative of operation of the rented vehicle by the driver during a period of time, is also received. By analyzing the telematics data, one or more driving behaviors of the driver during the time period is/are identified. One or more characteristics of the rented vehicle are also determined. One or more penalties or incentives are caused to be applied to the driver, based on the driving behavior(s) and the one or more characteristics of the rented vehicle.

Description

CROSS REFERENCE TO RELATED APPLICATION
This application is a continuation of, and claims priority to U.S. patent application Ser. No. 16/267,047, filed on Feb. 4, 2019, and entitled, “Determining Acceptable Driving Behavior Based on Vehicle Specific Characteristics,” the entire contents of which is incorporated herein by reference.
FIELD OF THE DISCLOSURE
The present disclosure generally relates to vehicle telematics and, more specifically, to systems and methods for applying one or more penalties and/or incentives to a driver of a rented vehicle based on telematics data.
BACKGROUND
Current technologies make use of vehicle telematics data to assess driving behavior. For example, the telematics data may be collected and analyzed to determine the acceleration, braking and/or cornering habits of a driver of a vehicle, and the results of the analysis may be used to measure the performance of the driver over time. The telematics data may be generated by sensors on the vehicle, or by a mobile device (e.g., smart phone) carried by the driver, for example. The measured performance may then be used for various purposes, such as modifying an insurance rating of the driver. More recently, it has been proposed that telematics data also be used in connection with car rental services, including peer-to-peer car rentals, to score potential renters (e.g., so that vehicle owners may avoid renting their vehicles to certain types of drivers).
Typically, car rental services utilize terms of a rental agreement to constrain renter use of the rented vehicle. However, such terms are not able to constrain or limit many driving behaviors of a renter while the renter is operating a vehicle. This may occur because car rental services are not able to detect such driving behaviors, and as a result the car rental services cannot incorporate such driving behaviors into a rental agreement. Thus, car rental services may rely on the vehicle owner trusting the renter to not misuse the rented vehicle. However, vehicle owners may hesitate to trust renters they do not personally know. Unconstrained use of the rented vehicle on the part of the renter may result in increased maintenance and the associated costs and time for the vehicle owner. For example, frequent hard braking by renters may require that the owner replace the brake pads and rotors more often. This is especially problematic for vehicle owners in peer-to-peer vehicle rental services. For these vehicle owners, the rented vehicle may be their primary vehicle, and associated costs and times of maintenance may be unmanageable.
For these reasons, there is a need for systems and methods that mitigate the problems of vehicle owner hesitation with respect to renting a vehicle and facilitate the rental of more vehicles for the vehicle renters with less risk to the vehicle owners.
SUMMARY
The present embodiments may, inter alia, utilize telematics data indicative of operation of a rented vehicle by the driver/renter during a period of time to apply one or more penalties and/or incentives to the driver, as agreed upon in the terms for renting the vehicle from the owner. In this way, the embodiments may establish trust between the owner of the vehicle and renter (or at least, provide the owner with a certain comfort level even if the renter cannot be fully trusted) by detecting and incentivizing or penalizing driving behaviors.
In one aspect, a method of incentivizing and/or penalizing vehicle renters may include receiving, at one or more processors, an indication that a driver has agreed to terms for renting a vehicle from an owner of the vehicle. The terms may include potential application of penalties or incentives to the driver based on driving behavior. The method may also include receiving, at the one or more processors, telematics data collected over a period of time. The telematics data may be indicative of operation of the rented vehicle by the driver during the period of time. The method may also include identifying, by the one or more processors analyzing the telematics data, one or more driving behaviors of the driver during the period of time. The method may further include identifying, by the one or more processors, one or more characteristics of the rented vehicle. The method may also include causing, by the one or more processors, one or more penalties or incentives to be applied to the driver. The one or more penalties may be based on the one or more driving behaviors and the one or more characteristics of the rented vehicle.
In another aspect, a tangible, non-transitory computer-readable medium may store instructions that, when executing by one or more processors, cause one or more processors to receive an indication that a driver has agreed to terms for renting a vehicle from an owner of the vehicle. The terms may include potential application of penalties or incentives to the driver based on driving behavior. The instructions may also cause the one or more processors to receive telematics data collected over a period of time. The telematics data may be indicative of operation of the rented vehicle by the driver during the period of time. The instructions may also cause the one or more processors to identify one or more driving behaviors of the driver during the period of time. The instructions may also cause the one or more processors to identify one or more characteristics of the rented vehicle. The instructions may also cause the one or more processors to cause one or more penalties or incentives to be applied to the driver. The one or more penalties may be based on the one or more driving behaviors and the one or more characteristics of the rented vehicle.
BRIEF DESCRIPTION OF THE DRAWINGS
The figures described below depict various aspects of the systems and methods disclosed herein. It should be understood that each figure depicts an embodiment of a particular aspect of the disclosed systems and methods, and that each of the figures is intended to accord with one possible embodiment thereof.
FIG. 1 illustrates an exemplary environment in which penalties and/or incentives may be applied to a renter based on telematics data.
FIG. 2 illustrates an exemplary chart of characteristics of a rented vehicle.
FIG. 3A illustrates an exemplary user interface by which a vehicle owner may establish vehicle rental terms.
FIG. 3B illustrates an exemplary user interface at which a vehicle owner may be notified that one or more penalties and/or incentives have been applied.
FIG. 4 illustrates an exemplary method of which may cause one or more penalties and/or incentives to be applied.
DETAILED DESCRIPTION
Aspects of the present invention relate to vehicle rental platforms wherein telematics data indicative of operation of the rented vehicle by a driver/renter may be used to apply one or more penalties and/or incentives to the driver, e.g., as agreed upon in the rental terms. The vehicle rental platform may be a peer-to-peer vehicle rental platform or a business to consumer vehicle rental platform, for example.
A vehicle owner may establish terms for renting a vehicle, e.g., via a user interface of the vehicle rental platform. A potential renter of the owner's vehicle may have access to such terms before agreeing to rent the owner's vehicle. A potential vehicle renter may be required to agree to the established terms before renting the owner's vehicle. If a renter has agreed to the terms for renting the owner's vehicle, the vehicle owner may receive an indication that the renter has agreed to the terms. The indication may include the terms themselves.
At some point after the vehicle renter and vehicle owner have agreed to the terms, a telematics data collection device, at the vehicle, may collect telematics data. Telematics data may only be collected, stored and/or used if the individual (i.e., renter or owner) has authorized such collection/storage/use. Authorization may occur, for example, upon the renter and the owner signing up to the vehicle rental platform. The telematics data collection device may include one or more components of a sensor/telematics system within the rented vehicle and/or may include a personal electronic device (e.g., a smart phone) of the renter. The telematics data collection device may also include sensors and/or subsystems that generate the telematics data itself, or may simply collect the telematics data after another device or devices has/have generated the telematics data. The telematics data may include any sensed or monitored data that indicates the operation of the rented vehicle by the driver. The telematics data collection device may collect data over a period of time during which the renter is operating the rented vehicle. The period of time may be the duration of a trip of the rented vehicle or a duration of a rental period of the rented vehicle, for example.
A remote server (i.e., remote from the telematics data collection device) may identify one or more driving behaviors occurring during the period of time, with the driving behavior(s) being determined from the telematics data. The driving behavior(s) may be associated with turning behavior, braking behavior, and/or accelerating behavior, for example. Identifying the driving behavior(s) may include identifying one or more values associated with the driving behavior(s), such as values associated with acceleration, speed, and/or lateral force. As the term is used herein, “acceleration” may refer to either positive or negative acceleration (i.e., deceleration), unless the context of the usage makes it clear that only positive or only negative acceleration is contemplated. The value(s) may be telematics data values (e.g., sensor readings, operational parameters of the vehicle such as speed, etc.) or normalized or averaged telematics data values, for example. The one or more values may be values calculated using telematics data (e.g., a weighted sum of multiple telematics data values). As the term is used herein, a “driving behavior” may be a behavior that occurs during a particular moment in time (e.g., a single hard braking event), or a behavior that occurs over a longer duration of time (e.g., an average number of hard braking events per hour).
Each rented vehicle may have one or more vehicle characteristics (e.g., make, model, a particular design such as rear-wheel or all-wheel drive, a particular feature such as cruise control, vehicle age, number of miles driven, maintenance). The remote server may determine the one or more characteristics of the rented vehicle based on telematics data, such as sensor data associated with the rented vehicle. The remote server may also, or instead, determine characteristics of the rented vehicle from data received from the vehicle owner or a third party. The remote server may obtain the one or more characteristics of the rented vehicle.
Based on the driving behavior and characteristic(s) of the rented vehicle, the remote server may apply one or more penalties and/or incentives to the renter operating the rented vehicle. These penalties and/or incentives may be in accordance with the agreed upon terms between the vehicle renter and the vehicle owner. The remote server may apply the one or more penalties and/or incentives immediately after a driving behavior occurs, or at a later time. The remote server may apply the one or more penalties and/or incentives in response to a single instance of a driving behavior, or in response to multiple instances of a driving behavior or multiple instances of different driving behaviors. Causing an incentive and/or penalty to be applied to the driver may include comparing one or more values associated with the driving behavior(s) (as discussed above) to one or more threshold values. For example, a penalty may be applied if the one or more values fall outside a threshold value, and/or an incentive may be applied if the one or more values stay within a threshold value.
Threshold values may vary based on the one or more characteristics of the rented vehicle. For example, a threshold value for “negative acceleration” (which may be indicative of how “hard” the driver brakes) may be lower for a sedan than for a sports car, or a threshold value for “negative acceleration” may be lower for a minivan than for a sedan, etc. Threshold values may also vary based on characteristics of a renter operating the rental vehicle. For example, a threshold value for “g-force” may be lower for a young driver than for an middle-aged driver. The threshold values may be associated with one or more of acceleration, speed, or lateral force of the rental vehicle during operation by the renter, for example.
If a penalty and/or an incentive has been applied, the remote server may transmit an electronic message to the owner of the rented vehicle notifying the owner that a penalty and/or incentive has been applied to the renter operating the rented vehicle. The remote server may transmit the notification to the owner immediately after the penalty and/or incentive has been applied. The remote server may instead transmit the notification at the end of the rental period by the renter, or at the next instance of a regular interval of time during the rental period. In other embodiments, the notification may be transmitted at any other suitable time.
If a penalty and/or an incentive has been applied, the remote server may transmit an electronic message to the renter of the rented vehicle notifying the renter that a penalty and/or incentive has been applied to the renter. The remote server may transmit the notification to the renter immediately after the penalty and/or incentive has been applied, at the end of the rental period by the renter, or at the next instance of a regular interval of time during the rental period. Alternatively, the remote server may transmit the notification when the rented vehicle is known to be stopped, such as when the vehicle is at a traffic light or the rented vehicle is parked. The vehicle may be determined to be stopped using telematics data (e.g., time stamped data from a GPS unit of the renter's mobile device or the vehicle, or data from an inertial measurement unit of the renter's mobile device or the vehicle). In other embodiments, the notification may be applied at any suitable other time.
FIG. 1 depicts an exemplary environment 100 in which penalties and/or incentives may be applied to a renter based on telematics data. As illustrated in FIG. 1, the environment 100 includes a vehicle 114. The vehicle 114 may include a data collection device (not shown in FIG. 1) that collects various types of telematics data. The vehicle 114 may also include multiple sensors (not shown in FIG. 1) that collect various types of telematics data. The vehicle 114 may also carry a renter mobile device 120 and the renter mobile device 120 may collect various types of telematics data.
The telematics data collected at the vehicle 114 may include any data that may be sensed or monitored, and may be used to calculate or otherwise infer driving behaviors of the renter. For example, the data may include any one or more of speed information, acceleration information, braking information, steering information, location/position information (e.g., collected by a vehicular global positioning system (GPS) device), translational and/or rotational g-force information (e.g., collected by a gyroscope device), on-board diagnostic information, information collected by a camera, video camera, LiDAR, radar or other device sensing an environment external to the vehicle (e.g., sensing proximity to other vehicles or other objects, orientation with respect to other vehicles or other objects, etc.), automated safety and/or control system information (e.g., adaptive cruise control status and/or when cruise control is engaged/disengaged, forward and/or rear collision warning system outputs, lane departure system outputs, electronic stability control system status, etc.), and so on. In some implementations, however, the vehicle telematics data includes at least acceleration and location data for the vehicle 114.
The vehicle 114, and/or the renter mobile device 120, may send collected telematics data to a server 102 via a communication link. Additionally or alternatively, the renter mobile device 120 may send collected telematics data to the server 102 via a communication link. For example, the vehicle 114 may send collected telematics data over a first communication link and the renter mobile device 120 may send collected telematics data over a second communication link. Additionally, the vehicle 114 and/or the renter mobile device 120 may send collected telematics data to server 102 via one or more transmissions. For example, telematics data telematics data may be collected at vehicle 114 and/or renter mobile device 120 over the course of a rented vehicle trip and sent to the server 102 in a single transmission. Alternatively, telematics data may be collected at vehicle 114 and/or renter mobile device 120 at regular intervals and sent to the server 102 in multiple transmissions (e.g., after each regular interval).
The transmissions may be sent to server 102 via a wireless transmitter or transceiver that is coupled to the vehicle 114. Alternatively, the vehicle 114 may be equipped with a Bluetooth system that provides the telematics data to a smart phone or other portable communication device of the driver or a passenger, such as renter mobile device 120, and the smart phone or other portable communication device may transmit the data to the server 102 via a wireless (e.g., cellular) network. In other implementations, the vehicle 114 may include an interface to a portable memory device, such as a portable hard drive or flash memory device. In some of these implementations, the portable memory device may be used to download data from the data collection device and then manually carried to the server 102. In still other implementations, the portable memory device may be used to download telematics data from the data collection device to a driver's or passenger's computer device (e.g., a desktop computer, laptop computer, smartphone, etc.), which may in turn be used to transmit the telematics data to the server 102 via one or more wired and/or wireless networks.
The server 102 may include a processor 104, a memory 106, a communication interface 108, a telematics data analysis unit 110, and a penalty/incentive application unit 112, each of which will be described in more detail below. The processor 104 may include one or more processors (e.g., a central processing unit (CPU)) adapted and configured to execute various software applications and components of the server 102. The memory 106 may include one or more memories of one or more types (e.g., a solid state memory, a hard drive, etc.), and may include data storage containing a plurality of software applications, data used and/or output by such software applications, and/or a plurality of software routines, for example.
The communication interface 108 may include hardware (e.g., one or more physical ports, one or more network interface cards, one or more hardware or firmware processors, etc.) and/or software (e.g., software executed by the processor 104 or one or more other processors of the server 102) configured to enable server 102 to receive transmissions of data collected by different data collection devices and mobile devices (e.g., associated with different vehicles similar to the vehicle 114). If the data transmissions are made via the Internet, for example, the communication interface 108 may include an Ethernet port. Telematics data may be received at the communication interface 108 from the vehicle 114, the renter mobile device 120, or any other device located within the vehicle 114.
Generally, the telematics data analysis unit 110 may analyze the received telematics data. Telematics data analysis unit 110 may only receive and analyze telematics data corresponding to a rental duration of the rented vehicle by the renter, for example. Alternatively, the telematics data unit 110 may filter received telematics data to exclude any telematics data that does not correspond to the rental duration. By analyzing the telematics data, the telematics data analysis unit 110 may determine one or more driving behaviors of the renter during the period of time, and may determine a time and/or a location associated with each such driving behavior.
The telematics data analysis unit 110 may also determine one or more characteristics of the vehicle 114 using telematics data of the vehicle 114. For example, the odometer may indicate the number of miles driven by the vehicle 114. Sensors on or in the tires may indicate the quality of the tires, the air pressure, and/or the date of last replacement (e.g., abrupt changes in the rate of tire spinning may indicate worn tires), for example. Additionally or alternatively, telematics data analysis unit 110 may determine the one or more characteristics of the vehicle 114 by analyzing data received from the rented vehicle characteristics server 116. The rented vehicle characteristics server 116 may store or otherwise provide access to rented vehicle characteristics information, and may include only a single server or multiple servers. The rented vehicle characteristics server 116 may be owned and operated by a third party, for example. The telematics data analysis unit 110 may determine one or more characteristics of the vehicle 114 by using an identifier of the vehicle 114 (e.g., a VIN of the vehicle 114) to locate matching data in a database maintained by the rented vehicle characteristics server 116.
Based on the driving behavior(s) and one or more characteristics of the rented vehicle, the penalty/incentive application unit 112 may apply one or more penalties and/or incentives to the driver of the rented vehicle 114. The penalty/incentive application unit 112 may compare values associated with the driving behavior(s) to threshold values. Initially, in some embodiments, the owner of the vehicle 114 initially sets the threshold values (e.g., via the owner mobile device 122 when executing a peer-to-peer vehicle sharing application). Alternatively, server 102 (e.g., the penalty/incentive application unit 112) may initially set the thresholds to default values. In either embodiment, the threshold values may then be modified based on the one or more characteristics of the rented vehicle 114 before being agreed upon by the owner and the renter as terms of the rental. In other embodiments, the server 102 (e.g., the penalty/incentive application unit 112) uses the one or more characteristics of the rented vehicle 114 to initially set the threshold values, rather than modifying previously set threshold values. A penalty or incentive may be applied if the values associated with the driving behavior fall within or exceed the threshold values. The penalty and/or incentive may be applied to an account of the renter associated with or linked to the vehicle rental platform, for example.
In some embodiments, one, some or all of the threshold values may be automatically set or modified based on the one or more characteristics of the rented vehicle 114 in which the driving behavior or set of driving behaviors occurred. For example, the threshold values may be set or modified to reflect that hard braking behavior is less acceptable because the vehicle 114 is known to have poor quality brakes (e.g., as detected by a sensor at the brakes of the vehicle 114) relative to most other vehicles. In this example, the threshold value for “negative acceleration” may be lowered, or set to a relatively low level. As another example, a threshold value for “negative acceleration” may be lower for a sedan than for a sport vehicle, and lower still for a minivan than a sedan.
The penalty/incentive application unit 112 may optionally utilize a driving score to determine if a penalty and/or incentive should be applied. Such driving scores may be calculated and stored by a driving score server 118, and the server 102 may access the driving scores by communicating with the driving score server 118 (e.g., via a long-range communication network such as the Internet). In other embodiments, the penalty/incentive application unit 112 or another component of the server 102 may calculate a driving score, and the server 102 may store the score in memory 106 or another memory. In still other embodiments, the penalty/incentive application unit 112 calculates a driving score but the driving score server 118 stores the driving score. In such an embodiment, the server 102 may transmit the driving score to the driving score server 118 to be stored.
The driving score may be calculated based on the driving behavior(s), and possibly also the one or more characteristics of the rented vehicle. For example, an instance of hard braking may result in a lower driving score if the instance of hard braking occurred in a minivan as opposed to a sedan, and/or an instance of excessive acceleration may result in a lower driving score if the acceleration occurred in a vehicle that needs tire replacement as opposed to a vehicle on which the tires were recently replaced. In some embodiments where the driving score itself accounts for one or more characteristics of the rented vehicle, penalty/incentive application unit 112 need not modify any of its thresholds (discussed above) based on the one or more characteristics of the rented vehicle.
The penalty/incentive application unit 112 may optionally notify the owner of the vehicle 114 that a penalty or an incentive has been applied to the renter of the vehicle 114. The server 102 may send this notification to the owner mobile device 122, for example. The server 102 and more specifically the communication interface 108 may transmit the notification to the owner mobile device 122 immediately after the penalty and/or incentive has been applied, at the end of the rental period by the renter, or at the next instance of a regular interval of time during the rental period, for example. In other embodiments, the server 102 may send the notification at any suitable other time.
The penalty/incentive application unit 112 may also, or instead, notify the driver of the vehicle 114 that a penalty or an incentive has been applied to the renter. The server 102 may send this notification to the owner mobile device 120, for example. The server 102 and more specifically the communication interface 108 may transmit the notification to the renter mobile device 120 immediately after the penalty and/or incentive has been applied, at the end of the rental period by the renter, or at the next instance of a regular interval of time during the rental period, for example (e.g., with the same timing as notifications to the vehicle owner). Alternatively, the server 102 may transmit the notification to the driver when the rented vehicle 114 is known to be stopped, such as when the vehicle 114 is at a traffic light or parked. The server 102 may determine that the vehicle 114 is stopped using telematics data (e.g., time stamped data from a GPS unit of the renter mobile device 120 or the vehicle 114, or data from an inertial measurement unit of the renter mobile device 120 or the vehicle 114). In other embodiments, the server 102 may send the notification at any suitable other time.
Operation of the components of the environment 100 will now be described in connection with FIGS. 2 through 4, according to various implementations and scenarios. Referring first to FIG. 2, an exemplary chart 200 of characteristics of a rented vehicle is shown. As shown, vehicle characteristics 202 may include a make 204, a model 206, an age 208 of the vehicle, a total number of miles 210 traveled by the vehicle, a time 212 within which the last oil change was obtained for the vehicle, a number of miles 214 since the last oil change was obtained for the vehicle, a time 216 within which the last tire replacement was obtained for the vehicle, a number of miles 218 since the last tire replacement was obtained for the vehicle, a time 220 within which the last tune up was obtained for the vehicle, and a number of miles 222 since last tune up was obtained for the vehicle. In additional embodiments, more or fewer vehicle characteristics may be included. Any number of the one or more vehicle characteristics may be utilized to cause a penalty and/or incentive to be applied. For example, the penalty or incentive application may be based on the make 204 and the model 206 of the vehicle only, or based on the make 204, the model 206, and the age 208 of the vehicle, etc. Similarly, any number of the one or more vehicle characteristics may be utilized to set or modify threshold values. For example, a threshold value for negative acceleration may be lower for a vehicle identified from the make 204 and the model 206 to be a minivan than a vehicle identified from the make 204 and the model 206 to be a sedan.
Each characteristic of the rented vehicle may have an associated value. For example, the make 204 has an associated value 224, the model 206 has an associated value 226, the age 208 has an associated value 228, the number of miles 210 has an associated value 230, the time 212 has an associated value 232, the number of miles 214 has an associated value 234, the time 216 has an associated value 236, the number of miles 218 has an associated value 238, the time 220 has an associated value 240, and the number of miles 222 has an associated value 242. Each associated value may be an exact value or it may be a range of values. For example, the age 208 of the vehicle may be indicated as 10 years, as 10-12 years, as the purchase date of the vehicle, and so on.
Each vehicle characteristic may change and be updated at any given point. For example, the number of miles 210 driven by the vehicle may change as the vehicle continues to be driven. This may be recorded in the telematics data and updated at the rented vehicle characteristics server 116, for example.
FIG. 3A illustrates an exemplary user interface 300 by which a vehicle owner may establish vehicle rental terms. The exemplary user interface 300 may be presented via an owner's mobile device such as owner mobile device 122 or any other device of the vehicle owner. The vehicle owner may establish vehicle rental terms by modifying driving behavior settings 302. The vehicle owner may establish vehicle rental terms at any time. The driving behavior settings 302 may include settings of a turning behavior control 304, settings of a braking behavior control 306, and settings of an accelerating behavior control 308. The driving behavior settings 302 may include settings of fewer, more and/or different controls than those shown in FIG. 3A. The vehicle owner may use the driving behavior settings 302 to establish vehicle rental terms. For example, the vehicle owner may establish acceptable turning behavior by modifying the turning behavior control 304. Acceptable turning behavior may be constrained, for example, by acceptable g-force on the vehicle. Driving behavior settings 302 may be translated to threshold values by server 102, for example. An owner of the rented vehicle may set specific penalties and or incentives at the driving behavior settings 302 and more specifically at the turning behavior control 304, braking behavior control 306, and accelerating behavior control 308 options. For example, a vehicle owner whose vehicle has a delicate steering system may opt to set stricter penalties with the turning behavior control 304 than with other behavior controls (e.g., a vehicle owner may set a monetary fine of twenty-five dollars per instance of unacceptable turning behavior).
Exemplary user interface 300 may also provide other settings 310 that an owner of the rented vehicle may modify. In the embodiment shown, the settings 310 include settings of a geolocation preferences control 312 and a vehicle use preferences control 314. For example, using the geolocation preferences control 312, a vehicle owner may set a geolocation boundary on where the rented vehicle may be driven (e.g., a vehicle owner in the suburbs of a large city may wish to set a geolocation preference such that the rented vehicle stays in the suburbs and is not driven into the city). Using the vehicle use preferences control 314, an owner of the rented vehicle may set preferences more generally as to how a vehicle may be used (e.g., with respect to actions other than driving behaviors or location). For example, an owner may set preferences regarding the presence of children and/or pets in the vehicle, and/or whether the vehicle may be used for purposes such as moving heavy items (e.g., furniture or yard items). Such activity may be detected by the rented vehicle, for example vehicle 114, using a weight sensor at the seats or hatchback, a microphone, a camera, and/or any other suitable type of sensor(s). A renter of the rented vehicle may be penalized or incentivized based on the settings of the turning behavior control 304, braking behavior control 306, accelerating behavior control 308, geolocation preferences control 312 and/or vehicle use preferences control 314, in combination with the corresponding portions of the telematics data.
The vehicle owner may also be able to modify or input information associated with the owner's account via an account settings control 316. The owner may make these modifications using a vehicle information control 318 and/or an account information control 320, for example. At the vehicle information control 318, the vehicle owner may input information regarding the one or more characteristics of the rented vehicle 114 (e.g., make, model, year, etc.). Input of such information may be required before the vehicle owner is able to offer the vehicle 114 for rent. The vehicle owner may further be prompted at vehicle information control 318 to periodically update information regarding the one or more characteristics of the rented vehicle 114.
Once the penalty and/or incentive has been applied, the server 102 may notify the renter and/or owner of the vehicle that the penalty has been applied. FIG. 3B illustrates an exemplary user interface 350 via which a vehicle owner may receive a notification 352 indicating that one or more penalties and/or incentives have been applied. The server 102 and more specifically the communication interface 108 may send notification 352 as soon as the penalty is applied, or at any other time. The notification 352 may include the reason why a penalty or incentive was applied (as depicted in FIG. 3B) or may not include such information.
The server 102 and more specifically the communication interface 108 may send a notification the same as or similar to notification 352 to the renter of the rented vehicle. The notification may optionally allow the renter to access more information about the penalty. The server 102 may send the notification to the renter immediately, or only when the rented vehicle is known to be stopped. The notification may be sent when the rented vehicle is known to be turned off, for example. Additionally, a notification that a penalty is about to be applied may be sent to the renter. For example, if the renter has agreed to a “three strike” system for a penalty, a notification that the renter has incurred the second “strike” may be delivered to the renter.
FIG. 4 is a flow diagram of an exemplary method 400 of incentivizing and/or penalizing a vehicle renter. In one embodiment, the method 400 may be implemented by the processor 104 of the server 102. As used herein, the term “server” may refer to a single computing device at a single location, or to a number of computing devices (e.g., distributed across a number of different locations).
In the method 400, an indication that a driver has agreed to terms for renting a vehicle from an owner of the vehicle may be received (block 402). The terms may include potential application of penalties or incentives to the driver based on driving behavior. The terms may be set by the owner of the rented vehicle, via a user interface accessed by the owner. For example, the owner may set the terms via the user interface 300 of FIG. 3A. The terms may specify preferences relating to one or more particular driving behaviors. The terms may specify certain instances in which driving behavior(s) is/are allowed or not allowed. The terms may further specify other vehicle owner preferences, such as geolocation preferences, and/or vehicle use preferences other than driving behaviors. Terms may vary based on a profile of a vehicle renter, and/or based on the rented vehicle.
The driver may accept the terms explicitly or implicitly. In some embodiments, the driver accepts the terms via a user interface similar to that shown in FIG. 3A. The terms may be transmitted to a sever implementing the method 400, such as server 102. The terms may be stored in the memory of the server (e.g., memory 106), and may be used by a software unit (e.g., the penalty/incentive application unit 112) to determine if a penalty and/or incentive should be applied.
Telematics data collected over a period of time may be received (block 404). The telematics data is indicative of the operation of the rented vehicle by the driver during the period of time. The data collection device may be similar to any of the different implementations discussed above (e.g., including one or multiple components, etc.). In some embodiments and/or scenarios, the vehicle telematics data includes a plurality of subsets of vehicle telematics data, with each subset corresponding to a different trip. The vehicle telematics data may be received by any suitable technique(s), such as any of the techniques for obtaining vehicle telematics data described above in connection with FIG. 1 (e.g., transferring to/from a portable memory, using wired and/or wireless communications, etc.), for example. The vehicle telematics data may include any data from which driving behaviors and/or features of the driving environment may be inferred or calculated. For example, the vehicle telematics data may include acceleration data, braking data, cornering data, g-force data, visual data, location data, etc., and may include data that was generated by an accelerometer, gyroscope, GPS device, camera, lidar, and/or one or more other units or sensor types.
The period of time may be the full duration of time during which the renter has rented the rented vehicle, the duration of a single trip by the renter in the rented vehicle, or any other duration of time. The telematics data may be received at the server implementing the method 400 (e.g., at server 102). The telematics data may be stored at the memory 106 and processed by the processor 104 (e.g., when executing instructions of the telematics data analysis unit 110), for example.
One or more driving behaviors of the driver during the period of time may be identified by analyzing the telematics data (block 406). The one or more driving behaviors may be identified by the server (e.g., by the telematics data analysis unit 110). The one of more driving behaviors may each correspond to certain portions of the telematics data, and may take place at a certain point in time (e.g., discrete driving events) or over a longer duration of time (e.g., average or maximum speeds, g-forces, etc., over a longer time period).
One or more characteristics of the rented vehicle may be identified (block 408). The one or more characteristics of the rented vehicle may be determined from the telematics data, and/or based on rented vehicle characteristics data received from the vehicle owner or a third party server. The one or more characteristics of the rented vehicle may include the make of the vehicle, the model of the vehicle, the age of the vehicle, the number of miles driven by the vehicle, the oil change status of the vehicle, the tire replacement status of the vehicle, the tune up status of the vehicle, and/or other characteristics.
One or more penalties or incentives may be applied (block 410). The application of the one or more penalties or incentives may be based on the one or more driving behaviors and the one or more characteristics of the rented vehicle. For example, the one or more driving behaviors may trigger a penalty when the behavior(s) occur(s) in a vehicle with certain characteristics, but not in a vehicle with other characteristics. The one or more driving behaviors may each correspond to one or more values (e.g., acceleration, speed, and/or lateral force). In some embodiments, the one or more penalties or incentives are applied if the one or more values fall outside (or stay within) a threshold value, wherein the threshold value may be based on the one or more characteristics of the rented vehicle (e.g., make of the rented vehicle, model of the rented vehicle, age of the rented vehicle, number of miles driven by the rented vehicle, and/or maintenance of the rented vehicle). Alternatively, the one or more penalties or incentives are applied if a driving score does not meet a threshold, with the driving score being computed based on the one or more characteristics of the rented vehicle. The renter of the vehicle and/or the owner of the vehicle may optionally be notified that a penalty has been applied (e.g., via the user interface of FIG. 3B).
The following additional considerations apply to the foregoing discussion. Throughout this specification, plural instances may implement operations or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.
Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.
As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).
In addition, use of “a” or “an” is employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the invention. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.
Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for a system and method for applying a penalty or an incentive to a driver of a rented vehicle based on telematics data. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims.
The patent claims at the end of this patent application are not intended to be construed under 35 U.S.C. § 112(f) unless traditional means-plus-function language is expressly recited, such as “means for” or “step for” language being explicitly recited in the claim(s).

Claims (20)

What is claimed:
1. A system comprising:
one or more processors; and
computer-readable media storing first computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving sensor data captured by a sensor located on a particular vehicle, the particular vehicle being associated with a unique identifier;
determining, based at least in part on the sensor data, a driving behavior associated with a driver of the particular vehicle, the driving behavior being characterized by at least one value;
receiving, from a device of an owner of the particular vehicle and separate from the particular vehicle, an input indicating a preference of the owner of the particular vehicle, wherein:
the preference is associated with the unique identifier of the particular vehicle and independent of the driver,
the preference corresponds to the driving behavior associated with the driver,
the owner of the particular vehicle is different from the driver, and
the particular vehicle is configured to operate independent of the preference;
determining a threshold value associated with the driving behavior based at least in part on the preference of the owner;
determining that the at least one value is less than the threshold value; and
applying an incentive to a record associated with the driver based at least in part on the at least one value being less than the threshold value.
2. The system of claim 1, wherein the sensor data comprises first sensor data, the driving behavior comprises a first driving behavior, the at least one value comprises a first value, and the threshold value comprises a first threshold value, and wherein the operations further comprising:
receiving second sensor data captured by the sensor located on the particular vehicle;
determining, based at least in part on the second sensor data, a second driving behavior associated with the driver of the particular vehicle, the second driving behavior being characterized by a second value;
determining a second threshold value associated with the second driving behavior and based at least in part on the preference of the owner;
determining that the second value meets or exceeds the second threshold value; and
applying a penalty to the record associated with the driver based at least in part on the second value meeting or exceeding the second threshold value.
3. The system of claim 1, wherein the driving behavior is associated with at least one of:
an acceleration;
a deceleration;
a speed; or
a lateral force.
4. The system of claim 1, the operations further comprising:
determining a characteristic of the particular vehicle, the characteristic comprising at least one of:
a make;
a model;
a type of drivetrain;
an age;
a number of miles driven; or
a feature associated with the vehicle,
wherein the threshold value is determined based at least in part on the characteristic.
5. The system of claim 1, wherein the sensor data comprises first sensor data captured by a first sensor, the operations further comprising:
receiving second sensor data captured by a second sensor located on the particular vehicle, the second sensor comprising a weight sensor;
determining, based at least in part on the second sensor data, a weight of a load placed inside the particular vehicle;
determining a threshold weight, based at least in part on the preference of the owner;
determining that the weight exceeds the threshold weight; and
determining a penalty to apply to the record associated with the driver based at least in part on the weight exceeding the threshold weight.
6. The system of claim 1, the operations further comprising:
sending a notification of the incentive to a first computing device associated with the driver and a second computing device associated with the owner of the particular vehicle.
7. The system of claim 1, wherein the sensor comprises at least one of a first sensor mounted on the particular vehicle or a second sensor of a mobile device of the driver.
8. The system of claim 1, the operations further comprising:
receiving an indication that the driver has agreed to terms of a contract corresponding to renting the particular vehicle from the owner of the particular vehicle,
wherein the terms include the preference of the owner.
9. The system of claim 1, wherein a mobile electronic device of the driver, carried by the particular vehicle, is configured to operate independent of the preference.
10. A method implemented at least in part by a server device associated with a peer-to-peer vehicle sharing service provider, the method comprising:
receiving sensor data captured by a sensor located on a particular vehicle, the particular vehicle being associated with a unique identifier;
determining, based at least in part on the sensor data, a driving behavior associated with a driver of the particular vehicle;
determining a value associated with the driving behavior;
receiving, from a device of an owner of the particular vehicle and separate from the particular vehicle, input indicating a preference of the owner of the particular vehicle, wherein:
the preference is associated with the unique identifier of the particular vehicle and independent of the driver,
the preference corresponds to the driving behavior,
the owner of the particular vehicle is different from the driver, and
the particular vehicle is configured to operate independent of the preference;
determining a threshold value associated with the driving behavior based at least in part on the preference of the owner; and
applying at least one of an incentive or a penalty to a record associated with the driver based at least in part on the value and the threshold value.
11. The method of claim 10, further comprising:
determining that the value associated with the driving behavior meets or exceeds the threshold value; and
applying a penalty to the record associated with the driver based at least in part on the value meeting or exceeding the threshold value.
12. The method of claim 10, further comprising:
determining that the value associated with the driving behavior is less than the threshold value; and
applying an incentive to the record associated with the driver based at least in part on the value being less than the threshold value.
13. The method of claim 10, further comprising:
receiving an indication that the driver has agreed to terms of a contract of the peer-to-peer vehicle sharing service corresponding to renting the particular vehicle,
wherein the terms include the preference of the owner and applying the at least one of the penalty or the incentive is based at least in part on the indication.
14. The method of claim 10, further comprising:
determining a characteristic of the particular vehicle, the characteristic comprising at least one of:
a make;
a model;
a type of drivetrain;
an age;
a number of miles driven; or
a feature associated with the vehicle,
wherein the determining the threshold value associated with the driving behavior is based at least in part on the characteristic.
15. The method of claim 10, wherein the preference of the owner corresponds to at least one of:
an acceleration of the particular vehicle;
a deceleration of the particular vehicle;
a maximum speed of the particular vehicle; or
a lateral force applied to the particular vehicle.
16. The method of claim 10, wherein the input is entered by the owner via a graphical user interface presented on the device.
17. One or more non-transitory computer readable media storing computer-executable instructions that, when executed by one or more processors of a computing device, cause the computing device to perform operations comprising:
receiving sensor data captured by a sensor located on a particular vehicle, the particular vehicle being associated with a unique identifier;
determining, based at least in part on the sensor data, a driving behavior associated with a driver of the particular vehicle;
determining a value associated with the driving behavior;
receiving, from a computing device of an owner of the particular vehicle and separate from the particular vehicle, an input indicative of a preference of the owner, wherein:
the preference is associated with the unique identifier of the particular vehicle and independent of the driver,
the preference corresponds to the driving behavior,
the owner of the particular vehicle is different from the driver, and
the particular vehicle is configured to operate independent of the preference;
determining a threshold value associated with the driving behavior based at least in part on the preference of the owner; and
applying at least one of an incentive or a penalty to a record associated with the driver based at least in part on the value and the threshold value.
18. The one or more non-transitory computer readable media of claim 17, the operations further comprising:
determining that the value associated with the driving behavior meets or exceeds the threshold value;
applying a penalty to the record associated with the driver based at least in part on the value meeting or exceeding the threshold value; and
sending a notification of the penalty to a computing device of the driver and the computing device of the owner.
19. The one or more non-transitory computer readable media of claim 17, the operations further comprising:
determining that the value associated with the driving behavior is less than the threshold value; and
applying an incentive to the record associated with the driver based at least in part on the value being less than the threshold value; and
sending a notification of the incentive or to a computing device of the driver and the computing device of the owner.
20. The one or more non-transitory computer readable media of claim 17, the operations further comprising:
receiving an indication that the driver has agreed to terms of a contract corresponding to renting the particular vehicle from the owner of the particular vehicle,
wherein the terms include the preference of the owner and applying the at least one of the penalty or the incentive is based at least in part on the indication.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220068044A1 (en) * 2020-08-28 2022-03-03 ANI Technologies Private Limited Driver score determination for vehicle drivers
US11961397B1 (en) * 2018-03-13 2024-04-16 Allstate Insurance Company Processing system having a machine learning engine for providing a customized driving assistance output

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11255683B1 (en) 2019-02-06 2022-02-22 State Farm Mutual Automobile Insurance Company First mile and last mile ride sharing method and system
US11900330B1 (en) 2019-10-18 2024-02-13 State Farm Mutual Automobile Insurance Company Vehicle telematics systems and methods
US20220176971A1 (en) * 2020-12-05 2022-06-09 Christian Nordh Driver Improvement

Citations (77)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050137757A1 (en) 2003-05-06 2005-06-23 Joseph Phelan Motor vehicle operating data collection and analysis
US20070008183A1 (en) * 2005-07-06 2007-01-11 Levi Yeshua R Method, system and device for detecting and reporting traffic law violations
US20080162373A1 (en) 2006-12-29 2008-07-03 Merrill Lynch & Co., Inc. System and Method for Monitoring Accounts With Insurance Benefits
US20080243558A1 (en) * 2007-03-27 2008-10-02 Ash Gupte System and method for monitoring driving behavior with feedback
US20090109037A1 (en) * 2000-08-11 2009-04-30 Telanon, Inc. Automated consumer to business electronic marketplace system
US20090210257A1 (en) * 2008-02-20 2009-08-20 Hartford Fire Insurance Company System and method for providing customized safety feedback
US20090222338A1 (en) * 2008-03-03 2009-09-03 Hamilton Ii Rick A Monitoring and Rewards Methodologies for "Green" Use of Vehicles
US20100030582A1 (en) * 2008-07-29 2010-02-04 Rippel Steven P Centrally maintained portable driving score
US20100087984A1 (en) * 2008-10-08 2010-04-08 Trimble Navigation Limited Devices, systems, and methods for monitoring driver and vehicle behavior
US20100106534A1 (en) * 2008-10-24 2010-04-29 Solid People Llc Certification and risk-management system and method for a rental agreement
US20100131307A1 (en) 2008-11-26 2010-05-27 Fred Collopy Monetization of performance information of an insured vehicle
US20110029358A1 (en) * 2009-07-29 2011-02-03 Searete Llc, A Limited Liability Corporation Of The State Of Delaware Promotional incentives based on hybrid vehicle qualification
US20110063099A1 (en) * 2008-06-27 2011-03-17 Ford Global Technologies, Llc System and method for recording vehicle events and for generating reports corresponding to the recorded vehicle events based on driver status
US20110087525A1 (en) * 2009-10-14 2011-04-14 International Business Machines Corporation Environmental stewardship based on driving behavior
US20110106591A1 (en) * 2009-04-30 2011-05-05 Searete Llc, A Limited Liability Corporation Of The State Of Delaware Awarding standings to a vehicle based upon one or more fuel utilization characteristics
US20110288891A1 (en) 2010-05-03 2011-11-24 Gettaround, Inc. On-demand third party asset rental platform
US20130013347A1 (en) * 1996-01-29 2013-01-10 Progressive Casualty Insurance Company Vehicle Monitoring System
US20130073112A1 (en) 2005-06-01 2013-03-21 Joseph Patrick Phelan Motor vehicle operating data collection and analysis
US20130217333A1 (en) * 2012-02-22 2013-08-22 Qualcomm Incorporated Determining rewards based on proximity of devices using short-range wireless broadcasts
US20130290199A1 (en) 2012-04-30 2013-10-31 General Motors Llc Monitoring and Aiding User Compliance with Vehicle Use Agreements
US20140019167A1 (en) 2012-07-16 2014-01-16 Shuli Cheng Method and Apparatus for Determining Insurance Risk Based on Monitoring Driver's Eyes and Head
US20140129113A1 (en) * 2012-11-07 2014-05-08 Ford Global Technologies, Llc Hardware and controls for personal vehicle rental
US20140129053A1 (en) 2012-11-07 2014-05-08 Ford Global Technologies, Llc Credential check and authorization solution for personal vehicle rental
US20140129301A1 (en) * 2012-11-07 2014-05-08 Ford Global Technologies, Llc Mobile automotive wireless communication system enabled microbusinesses
US20140142805A1 (en) * 2012-11-20 2014-05-22 General Motors Llc Method and system for in-vehicle function control
US20140210625A1 (en) * 2013-01-31 2014-07-31 Lytx, Inc. Direct observation event triggering of drowsiness
US20140229258A1 (en) * 2011-03-16 2014-08-14 Malak Seriani Systems and methods enabling transportation service providers to competitively bid in response to customer requests
US20140257874A1 (en) * 2013-03-10 2014-09-11 State Farm Mutual Automobile Insurance Company System and Method for Determining and Monitoring Auto Insurance Incentives
US20140372017A1 (en) * 2013-06-14 2014-12-18 Cartasite, Inc. Vehicle performance detection, analysis, and presentation
US20150057875A1 (en) * 2013-08-02 2015-02-26 Tweddle Group Systems and methods of creating and delivering item of manufacture specific information to remote devices
US20150081404A1 (en) 2013-08-12 2015-03-19 Intelligent Mechatronic Systems Inc. Driver behavior enhancement using scoring, recognition and redeemable rewards
US20150112546A1 (en) 2013-10-23 2015-04-23 Trimble Navigation Limited Driver scorecard system and method
US20150120083A1 (en) * 2013-10-31 2015-04-30 GM Global Technology Operations LLC Methods, systems and apparatus for determining whether any vehicle events specified in notification preferences have occurred
US20150178661A1 (en) 2013-12-19 2015-06-25 Trapeze Software Ulc System And Method For Analyzing Performance Data In A Transit Organization
US9087099B2 (en) 2013-05-29 2015-07-21 General Motors Llc Centrally managed driver and vehicle ratings system updated via over-the-air communications with telematics units
US20150213420A1 (en) 2014-01-28 2015-07-30 Nissan North America, Inc. Method and device for determining vehicle condition based on operational factors
US20150213519A1 (en) * 2014-01-28 2015-07-30 Nissan North America, Inc. Method and device for determining vehicle condition based on non-operational factors
US20150254955A1 (en) * 2014-03-07 2015-09-10 State Farm Mutual Automobile Insurance Company Vehicle operator emotion management system and method
US9135803B1 (en) * 2014-04-17 2015-09-15 State Farm Mutual Automobile Insurance Company Advanced vehicle operator intelligence system
US20150324923A1 (en) * 2014-05-08 2015-11-12 State Farm Mutual Automobile Insurance Company Systems and methods for identifying and assessing location-based risks for vehicles
US20150371153A1 (en) 2014-06-24 2015-12-24 General Motors Llc Vehicle Sharing System Supporting Nested Vehicle Sharing Within A Loan Period For A Primary Vehicle Borrower
US20160014062A1 (en) * 2014-07-08 2016-01-14 Rinkesh Patel System and method for blocking or delaying notifications on a mobile device while simultaneously allowing approved integrated functionality
US20160102840A1 (en) 2013-04-26 2016-04-14 Zumtobel Lighting Gmbh Flat reflector element for an led circuit board
US9520006B1 (en) * 2014-08-28 2016-12-13 Allstate Insurance Company Vehicle diagnostics
US20160364812A1 (en) * 2015-06-11 2016-12-15 Raymond Cao Systems and methods for on-demand transportation
US20170076395A1 (en) 2014-03-03 2017-03-16 Inrix Inc. User-managed evidentiary record of driving behavior and risk rating
US20170098231A1 (en) 2015-10-01 2017-04-06 GM Global Technology Operations LLC Systems to subsidize vehicle-ownership rights based on system-driver interactions
US20170206717A1 (en) 2016-01-19 2017-07-20 Trendfire Technologies, Inc. System and method for driver evaluation, rating, and skills improvement
WO2017142536A1 (en) 2016-02-18 2017-08-24 Ford Global Technologies, Llc Cloud-based dynamic vehicle sharing system and method
US20170291611A1 (en) * 2016-04-06 2017-10-12 At&T Intellectual Property I, L.P. Methods and apparatus for vehicle operation analysis
US20170301220A1 (en) * 2016-04-19 2017-10-19 Navio International, Inc. Modular approach for smart and customizable security solutions and other applications for a smart city
US20170305434A1 (en) * 2016-04-26 2017-10-26 Sivalogeswaran Ratnasingam Dynamic Learning Driving System and Method
US9805601B1 (en) * 2015-08-28 2017-10-31 State Farm Mutual Automobile Insurance Company Vehicular traffic alerts for avoidance of abnormal traffic conditions
US20170365169A1 (en) 2014-12-02 2017-12-21 Here Global B.V. Method And Apparatus For Determining Location-Based Vehicle Behavior
US20180061232A1 (en) * 2016-08-29 2018-03-01 Allstate Insurance Company Electrical Data Processing System for Determining Status of Traffic Device and Vehicle Movement
US20180075380A1 (en) 2016-09-10 2018-03-15 Swiss Reinsurance Company Ltd. Automated, telematics-based system with score-driven triggering and operation of automated sharing economy risk-transfer systems and corresponding method thereof
US20180097883A1 (en) 2016-09-30 2018-04-05 The Toronto-Dominion Bank Automated implementation of provisioned services based on captured sensor data
US20180108058A1 (en) * 2016-10-18 2018-04-19 Autoalert, Llc Visual discovery tool for automotive manufacturers with network encryption, data conditioning, and prediction engine
US20180158329A1 (en) 2016-12-06 2018-06-07 Acyclica Inc. Methods and apparatus for monitoring traffic data
US20180154867A1 (en) 2015-06-12 2018-06-07 Phrame, Inc. System and methods for vehicle sharing
US20180174457A1 (en) * 2016-12-16 2018-06-21 Wheego Electric Cars, Inc. Method and system using machine learning to determine an automotive driver's emotional state
US20180178797A1 (en) * 2016-12-22 2018-06-28 Blackberry Limited Adjusting mechanical elements of cargo transportation units
US20180240153A1 (en) * 2015-01-22 2018-08-23 Empire Technology Development Llc Beacon implementation
US20180293687A1 (en) * 2017-04-10 2018-10-11 International Business Machines Corporation Ridesharing management for autonomous vehicles
US10157422B2 (en) * 2007-05-10 2018-12-18 Allstate Insurance Company Road segment safety rating
US20190066409A1 (en) * 2017-08-24 2019-02-28 Veniam, Inc. Methods and systems for measuring performance of fleets of autonomous vehicles
US20190066535A1 (en) 2017-08-18 2019-02-28 Tourmaline Labs, Inc. System and methods for relative driver scoring using contextual analytics
US20190111933A1 (en) * 2016-03-29 2019-04-18 Avl List Gmbh Method for producing control data for rule-based driver assistance
US20190196503A1 (en) * 2017-12-22 2019-06-27 Lyft, Inc. Autonomous-Vehicle Dispatch Based on Fleet-Level Target Objectives
US10388157B1 (en) * 2018-03-13 2019-08-20 Allstate Insurance Company Processing system having a machine learning engine for providing a customized driving assistance output
US20190291738A1 (en) * 2018-03-26 2019-09-26 International Business Machines Corporation Cognitive modeling for anomalies detection in driving
US10445758B1 (en) * 2013-03-15 2019-10-15 Allstate Insurance Company Providing rewards based on driving behaviors detected by a mobile computing device
US20200033868A1 (en) * 2018-07-27 2020-01-30 GM Global Technology Operations LLC Systems, methods and controllers for an autonomous vehicle that implement autonomous driver agents and driving policy learners for generating and improving policies based on collective driving experiences of the autonomous driver agents
US20200172112A1 (en) * 2018-12-03 2020-06-04 Honda Motor Co., Ltd. System and method for determining a change of a customary vehicle driver
US10885539B1 (en) * 2015-10-29 2021-01-05 Arity International Limited Driving points
US10956982B1 (en) * 2016-05-11 2021-03-23 State Farm Mutual Automobile Insurance Company Systems and methods for allocating vehicle costs between vehicle users for anticipated trips
US10984479B1 (en) * 2015-10-20 2021-04-20 United Services Automobile Association (Usaa) System and method for tracking the operation of a vehicle and/or the actions of a driver

Family Cites Families (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6856877B2 (en) * 2002-05-29 2005-02-15 Ford Global Technologies, Llc Integration of active assist and vehicle dynamics control and method
US7715961B1 (en) * 2004-04-28 2010-05-11 Agnik, Llc Onboard driver, vehicle and fleet data mining
US9067565B2 (en) * 2006-05-22 2015-06-30 Inthinc Technology Solutions, Inc. System and method for evaluating driver behavior
US8287055B2 (en) * 2010-09-28 2012-10-16 Robert Bosch Gmbh Brake control of a vehicle based on driver behavior
US20120089442A1 (en) * 2010-10-12 2012-04-12 SweMex LLC Driving while distracted accountability and rewards systems and methods
EP2752346B1 (en) * 2011-08-29 2019-10-23 Toyota Jidosha Kabushiki Kaisha Vehicle control device
US8738262B2 (en) * 2011-12-30 2014-05-27 Ford Global Technologies, Llc Driving behavior feedback interface
US8682557B2 (en) * 2011-12-30 2014-03-25 Ford Global Technologies, Llc Driving behavior feedback interface
US9536361B2 (en) * 2012-03-14 2017-01-03 Autoconnect Holdings Llc Universal vehicle notification system
US20150025917A1 (en) * 2013-07-15 2015-01-22 Advanced Insurance Products & Services, Inc. System and method for determining an underwriting risk, risk score, or price of insurance using cognitive information
US10133530B2 (en) * 2014-05-19 2018-11-20 Allstate Insurance Company Electronic display systems connected to vehicles and vehicle-based systems
US20160059775A1 (en) * 2014-09-02 2016-03-03 Nuance Communications, Inc. Methods and apparatus for providing direction cues to a driver
WO2017020142A1 (en) * 2015-08-03 2017-02-09 深圳市好航科技有限公司 Multi-purpose vehicle smart monitoring system and method
US9701279B1 (en) * 2016-01-12 2017-07-11 Gordon*Howard Associates, Inc. On board monitoring device
US10395438B2 (en) * 2016-08-19 2019-08-27 Calamp Corp. Systems and methods for crash determination with noise filtering
US20180108189A1 (en) * 2016-10-13 2018-04-19 General Motors Llc Telematics-based vehicle value reports
US10515390B2 (en) * 2016-11-21 2019-12-24 Nio Usa, Inc. Method and system for data optimization
US10421456B2 (en) * 2017-02-20 2019-09-24 Ford Global Technologies, Llc Customized electric parking brake response to maintain engine auto-stop with brake released
US20180285885A1 (en) * 2017-03-28 2018-10-04 Toyota Motor Engineering & Manufacturing North America, Inc. Modules, systems, and methods for incentivizing green driving
JP6575934B2 (en) * 2017-03-29 2019-09-18 マツダ株式会社 Vehicle driving support system and vehicle driving support method
US10580225B2 (en) * 2017-03-31 2020-03-03 Toyota Motor Engineering & Manufacturing North America, Inc. Privacy-aware signal monitoring systems and methods
US10438486B2 (en) * 2017-07-10 2019-10-08 Lyft, Inc. Dynamic modeling and simulation of an autonomous vehicle fleet using real-time autonomous vehicle sensor input
US10540892B1 (en) * 2017-10-26 2020-01-21 State Farm Mutual Automobile Insurance Company Technology for real-time detection and mitigation of remote vehicle anomalous behavior
US10977874B2 (en) * 2018-06-11 2021-04-13 International Business Machines Corporation Cognitive learning for vehicle sensor monitoring and problem detection

Patent Citations (78)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130013347A1 (en) * 1996-01-29 2013-01-10 Progressive Casualty Insurance Company Vehicle Monitoring System
US20090109037A1 (en) * 2000-08-11 2009-04-30 Telanon, Inc. Automated consumer to business electronic marketplace system
US20050137757A1 (en) 2003-05-06 2005-06-23 Joseph Phelan Motor vehicle operating data collection and analysis
US20130073112A1 (en) 2005-06-01 2013-03-21 Joseph Patrick Phelan Motor vehicle operating data collection and analysis
US20070008183A1 (en) * 2005-07-06 2007-01-11 Levi Yeshua R Method, system and device for detecting and reporting traffic law violations
US20080162373A1 (en) 2006-12-29 2008-07-03 Merrill Lynch & Co., Inc. System and Method for Monitoring Accounts With Insurance Benefits
US20080243558A1 (en) * 2007-03-27 2008-10-02 Ash Gupte System and method for monitoring driving behavior with feedback
US10157422B2 (en) * 2007-05-10 2018-12-18 Allstate Insurance Company Road segment safety rating
US20090210257A1 (en) * 2008-02-20 2009-08-20 Hartford Fire Insurance Company System and method for providing customized safety feedback
US20090222338A1 (en) * 2008-03-03 2009-09-03 Hamilton Ii Rick A Monitoring and Rewards Methodologies for "Green" Use of Vehicles
US20110063099A1 (en) * 2008-06-27 2011-03-17 Ford Global Technologies, Llc System and method for recording vehicle events and for generating reports corresponding to the recorded vehicle events based on driver status
US20100030582A1 (en) * 2008-07-29 2010-02-04 Rippel Steven P Centrally maintained portable driving score
US20100087984A1 (en) * 2008-10-08 2010-04-08 Trimble Navigation Limited Devices, systems, and methods for monitoring driver and vehicle behavior
US20100106534A1 (en) * 2008-10-24 2010-04-29 Solid People Llc Certification and risk-management system and method for a rental agreement
US20100131307A1 (en) 2008-11-26 2010-05-27 Fred Collopy Monetization of performance information of an insured vehicle
US20110106591A1 (en) * 2009-04-30 2011-05-05 Searete Llc, A Limited Liability Corporation Of The State Of Delaware Awarding standings to a vehicle based upon one or more fuel utilization characteristics
US20110029358A1 (en) * 2009-07-29 2011-02-03 Searete Llc, A Limited Liability Corporation Of The State Of Delaware Promotional incentives based on hybrid vehicle qualification
US20110087525A1 (en) * 2009-10-14 2011-04-14 International Business Machines Corporation Environmental stewardship based on driving behavior
US20110288891A1 (en) 2010-05-03 2011-11-24 Gettaround, Inc. On-demand third party asset rental platform
US20140229258A1 (en) * 2011-03-16 2014-08-14 Malak Seriani Systems and methods enabling transportation service providers to competitively bid in response to customer requests
US20130217333A1 (en) * 2012-02-22 2013-08-22 Qualcomm Incorporated Determining rewards based on proximity of devices using short-range wireless broadcasts
US20130290199A1 (en) 2012-04-30 2013-10-31 General Motors Llc Monitoring and Aiding User Compliance with Vehicle Use Agreements
US20140019167A1 (en) 2012-07-16 2014-01-16 Shuli Cheng Method and Apparatus for Determining Insurance Risk Based on Monitoring Driver's Eyes and Head
US20140129113A1 (en) * 2012-11-07 2014-05-08 Ford Global Technologies, Llc Hardware and controls for personal vehicle rental
US20140129053A1 (en) 2012-11-07 2014-05-08 Ford Global Technologies, Llc Credential check and authorization solution for personal vehicle rental
US20140129301A1 (en) * 2012-11-07 2014-05-08 Ford Global Technologies, Llc Mobile automotive wireless communication system enabled microbusinesses
US20140142805A1 (en) * 2012-11-20 2014-05-22 General Motors Llc Method and system for in-vehicle function control
US20140210625A1 (en) * 2013-01-31 2014-07-31 Lytx, Inc. Direct observation event triggering of drowsiness
US20140257874A1 (en) * 2013-03-10 2014-09-11 State Farm Mutual Automobile Insurance Company System and Method for Determining and Monitoring Auto Insurance Incentives
US10445758B1 (en) * 2013-03-15 2019-10-15 Allstate Insurance Company Providing rewards based on driving behaviors detected by a mobile computing device
US20160102840A1 (en) 2013-04-26 2016-04-14 Zumtobel Lighting Gmbh Flat reflector element for an led circuit board
US9087099B2 (en) 2013-05-29 2015-07-21 General Motors Llc Centrally managed driver and vehicle ratings system updated via over-the-air communications with telematics units
US20140372017A1 (en) * 2013-06-14 2014-12-18 Cartasite, Inc. Vehicle performance detection, analysis, and presentation
US20150057875A1 (en) * 2013-08-02 2015-02-26 Tweddle Group Systems and methods of creating and delivering item of manufacture specific information to remote devices
US20150081404A1 (en) 2013-08-12 2015-03-19 Intelligent Mechatronic Systems Inc. Driver behavior enhancement using scoring, recognition and redeemable rewards
US20150112546A1 (en) 2013-10-23 2015-04-23 Trimble Navigation Limited Driver scorecard system and method
US20150120083A1 (en) * 2013-10-31 2015-04-30 GM Global Technology Operations LLC Methods, systems and apparatus for determining whether any vehicle events specified in notification preferences have occurred
US20150178661A1 (en) 2013-12-19 2015-06-25 Trapeze Software Ulc System And Method For Analyzing Performance Data In A Transit Organization
US20150213519A1 (en) * 2014-01-28 2015-07-30 Nissan North America, Inc. Method and device for determining vehicle condition based on non-operational factors
US20150213420A1 (en) 2014-01-28 2015-07-30 Nissan North America, Inc. Method and device for determining vehicle condition based on operational factors
US20170076395A1 (en) 2014-03-03 2017-03-16 Inrix Inc. User-managed evidentiary record of driving behavior and risk rating
US20150254955A1 (en) * 2014-03-07 2015-09-10 State Farm Mutual Automobile Insurance Company Vehicle operator emotion management system and method
US9135803B1 (en) * 2014-04-17 2015-09-15 State Farm Mutual Automobile Insurance Company Advanced vehicle operator intelligence system
US20150324923A1 (en) * 2014-05-08 2015-11-12 State Farm Mutual Automobile Insurance Company Systems and methods for identifying and assessing location-based risks for vehicles
US20150371153A1 (en) 2014-06-24 2015-12-24 General Motors Llc Vehicle Sharing System Supporting Nested Vehicle Sharing Within A Loan Period For A Primary Vehicle Borrower
US20160014062A1 (en) * 2014-07-08 2016-01-14 Rinkesh Patel System and method for blocking or delaying notifications on a mobile device while simultaneously allowing approved integrated functionality
US9520006B1 (en) * 2014-08-28 2016-12-13 Allstate Insurance Company Vehicle diagnostics
US20170365169A1 (en) 2014-12-02 2017-12-21 Here Global B.V. Method And Apparatus For Determining Location-Based Vehicle Behavior
US20180240153A1 (en) * 2015-01-22 2018-08-23 Empire Technology Development Llc Beacon implementation
US20160364812A1 (en) * 2015-06-11 2016-12-15 Raymond Cao Systems and methods for on-demand transportation
US20180154867A1 (en) 2015-06-12 2018-06-07 Phrame, Inc. System and methods for vehicle sharing
US9805601B1 (en) * 2015-08-28 2017-10-31 State Farm Mutual Automobile Insurance Company Vehicular traffic alerts for avoidance of abnormal traffic conditions
US20210166323A1 (en) * 2015-08-28 2021-06-03 State Farm Mutual Automobile Insurance Company Determination of driver or vehicle discounts and risk profiles based upon vehicular travel environment
US20170098231A1 (en) 2015-10-01 2017-04-06 GM Global Technology Operations LLC Systems to subsidize vehicle-ownership rights based on system-driver interactions
US10984479B1 (en) * 2015-10-20 2021-04-20 United Services Automobile Association (Usaa) System and method for tracking the operation of a vehicle and/or the actions of a driver
US10885539B1 (en) * 2015-10-29 2021-01-05 Arity International Limited Driving points
US20170206717A1 (en) 2016-01-19 2017-07-20 Trendfire Technologies, Inc. System and method for driver evaluation, rating, and skills improvement
WO2017142536A1 (en) 2016-02-18 2017-08-24 Ford Global Technologies, Llc Cloud-based dynamic vehicle sharing system and method
US20190111933A1 (en) * 2016-03-29 2019-04-18 Avl List Gmbh Method for producing control data for rule-based driver assistance
US20170291611A1 (en) * 2016-04-06 2017-10-12 At&T Intellectual Property I, L.P. Methods and apparatus for vehicle operation analysis
US20170301220A1 (en) * 2016-04-19 2017-10-19 Navio International, Inc. Modular approach for smart and customizable security solutions and other applications for a smart city
US20170305434A1 (en) * 2016-04-26 2017-10-26 Sivalogeswaran Ratnasingam Dynamic Learning Driving System and Method
US10956982B1 (en) * 2016-05-11 2021-03-23 State Farm Mutual Automobile Insurance Company Systems and methods for allocating vehicle costs between vehicle users for anticipated trips
US20180061232A1 (en) * 2016-08-29 2018-03-01 Allstate Insurance Company Electrical Data Processing System for Determining Status of Traffic Device and Vehicle Movement
US20180075380A1 (en) 2016-09-10 2018-03-15 Swiss Reinsurance Company Ltd. Automated, telematics-based system with score-driven triggering and operation of automated sharing economy risk-transfer systems and corresponding method thereof
US20180097883A1 (en) 2016-09-30 2018-04-05 The Toronto-Dominion Bank Automated implementation of provisioned services based on captured sensor data
US20180108058A1 (en) * 2016-10-18 2018-04-19 Autoalert, Llc Visual discovery tool for automotive manufacturers with network encryption, data conditioning, and prediction engine
US20180158329A1 (en) 2016-12-06 2018-06-07 Acyclica Inc. Methods and apparatus for monitoring traffic data
US20180174457A1 (en) * 2016-12-16 2018-06-21 Wheego Electric Cars, Inc. Method and system using machine learning to determine an automotive driver's emotional state
US20180178797A1 (en) * 2016-12-22 2018-06-28 Blackberry Limited Adjusting mechanical elements of cargo transportation units
US20180293687A1 (en) * 2017-04-10 2018-10-11 International Business Machines Corporation Ridesharing management for autonomous vehicles
US20190066535A1 (en) 2017-08-18 2019-02-28 Tourmaline Labs, Inc. System and methods for relative driver scoring using contextual analytics
US20190066409A1 (en) * 2017-08-24 2019-02-28 Veniam, Inc. Methods and systems for measuring performance of fleets of autonomous vehicles
US20190196503A1 (en) * 2017-12-22 2019-06-27 Lyft, Inc. Autonomous-Vehicle Dispatch Based on Fleet-Level Target Objectives
US10388157B1 (en) * 2018-03-13 2019-08-20 Allstate Insurance Company Processing system having a machine learning engine for providing a customized driving assistance output
US20190291738A1 (en) * 2018-03-26 2019-09-26 International Business Machines Corporation Cognitive modeling for anomalies detection in driving
US20200033868A1 (en) * 2018-07-27 2020-01-30 GM Global Technology Operations LLC Systems, methods and controllers for an autonomous vehicle that implement autonomous driver agents and driving policy learners for generating and improving policies based on collective driving experiences of the autonomous driver agents
US20200172112A1 (en) * 2018-12-03 2020-06-04 Honda Motor Co., Ltd. System and method for determining a change of a customary vehicle driver

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Goodwin, "Policy Incentives to Change Behaviour in Passenger Transport," OECD International Transport Forum, Leipzig, May 2008, Transport and Energy: The Challenge of Climate Change. 1-34 (May 2008).
Griffiths, "Telematics is revolutionising fleet management", Connected Car, Financial Times. 1-4 (Apr. 18, 2016).
Hampshire et al., "Market Analysis and Potential Growth", Peer-to-Peer Carsharing, Transportation Research Record Journal of the Transportation Research Board. 119-126 (Dec. 2011).
Kantor et al., "Design of Algorithms for Payment Telematics Systems Evaluating Driver's Driving Style," Transactions of Transport Sciences, 7:9-16 (Jan. 2014).
Mortimer, et al., "The effect of ‘smart’ financial incentives on driving behaviour of novice drivers," Accident Analysis and Prevention, 119:68-79 (2018).

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11961397B1 (en) * 2018-03-13 2024-04-16 Allstate Insurance Company Processing system having a machine learning engine for providing a customized driving assistance output
US20220068044A1 (en) * 2020-08-28 2022-03-03 ANI Technologies Private Limited Driver score determination for vehicle drivers
US11798321B2 (en) * 2020-08-28 2023-10-24 ANI Technologies Private Limited Driver score determination for vehicle drivers

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